Articles | Volume 12, issue 1
https://doi.org/10.5194/essd-12-719-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-719-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
26 Mar 2020
Data description paper |
| 26 Mar 2020
| 26 Mar 2020
Laboratory, field, mast-borne and airborne spectral reflectance measurements of boreal landscape during spring
Henna-Reetta Hannula
CORRESPONDING AUTHOR
Space and Earth Observation Centre, Finnish Meteorological Institute,
Helsinki, 00560, Finland
Kirsikka Heinilä
Geoinformatics Research, Finnish Environment Institute, Helsinki,
00790, Finland
Kristin Böttcher
Geoinformatics Research, Finnish Environment Institute, Helsinki,
00790, Finland
Olli-Pekka Mattila
Programme for Environmental Information, Finnish Environment Institute,
Helsinki, 00790, Finland
Miia Salminen
Space and Earth Observation Centre, Finnish Meteorological Institute,
Helsinki, 00560, Finland
Jouni Pulliainen
Space and Earth Observation Centre, Finnish Meteorological Institute,
Helsinki, 00560, Finland
Related authors
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
Short summary
Short summary
Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Jonas Svensson, Johan Ström, Niku Kivekäs, Nathaniel B. Dkhar, Shresth Tayal, Ved P. Sharma, Arttu Jutila, John Backman, Aki Virkkula, Meri Ruppel, Antti Hyvärinen, Anna Kontu, Henna-Reetta Hannula, Matti Leppäranta, Rakesh K. Hooda, Atte Korhola, Eija Asmi, and Heikki Lihavainen
Atmos. Meas. Tech., 11, 1403–1416, https://doi.org/10.5194/amt-11-1403-2018, https://doi.org/10.5194/amt-11-1403-2018, 2018
Short summary
Short summary
Receding glaciers in the Himalayas are of concern. Here we present measurements of light-absorbing impurities, known to contribute to the ongoing glacier decrease, in snow from Indian Himalayas and compare them to snow samples from the Finnish Arctic. The soot particles in the snow are shown to have lower light absorbing efficiency, possibly affecting their radiative forcing potential in the snow. Further, dust influences the snow in the Himalayas to a much greater extent than in Finland.
Henna-Reetta Hannula, Juha Lemmetyinen, Anna Kontu, Chris Derksen, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 347–363, https://doi.org/10.5194/gi-5-347-2016, https://doi.org/10.5194/gi-5-347-2016, 2016
Short summary
Short summary
The paper described an extensive in situ data set of bulk snow depth, snow water equivalent, and snow density collected as a support of SnowSAR-2 airborne campaign in northern Finland. The spatial and temporal variability of these snow properties was analyzed in different land cover types. The success of the chosen measurement protocol to provide an accurate reference for the simultaneous SAR data products was analyzed in the context of spatial scale, sample size, and uncertainty.
Kaisa Lakkala, Hanne Suokanerva, Juha Matti Karhu, Antti Aarva, Antti Poikonen, Tomi Karppinen, Markku Ahponen, Henna-Reetta Hannula, Anna Kontu, and Esko Kyrö
Geosci. Instrum. Method. Data Syst., 5, 315–320, https://doi.org/10.5194/gi-5-315-2016, https://doi.org/10.5194/gi-5-315-2016, 2016
Short summary
Short summary
This paper describes the laboratory facilities at the Finnish Meteorological Institute – Arctic Research Centre (FMI-ARC). They comprise an optical laboratory, a facility for biological studies, and an office. The facilities are ideal for responding to the needs of international multidisciplinary research, giving the possibility to calibrate and characterize the research instruments as well as handle and store samples.
Leena Leppänen, Anna Kontu, Henna-Reetta Hannula, Heidi Sjöblom, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 163–179, https://doi.org/10.5194/gi-5-163-2016, https://doi.org/10.5194/gi-5-163-2016, 2016
Short summary
Short summary
The manual snow survey program of Finnish Meteorological Institute consists of numerous observations of natural seasonal snowpack in Sodankylä, in northern Finland. Systematic snow measurements began in 1911 with snow depth and snow water equivalent. In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from snow pits. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack.
William Maslanka, Leena Leppänen, Anna Kontu, Mel Sandells, Juha Lemmetyinen, Martin Schneebeli, Martin Proksch, Margret Matzl, Henna-Reetta Hannula, and Robert Gurney
Geosci. Instrum. Method. Data Syst., 5, 85–94, https://doi.org/10.5194/gi-5-85-2016, https://doi.org/10.5194/gi-5-85-2016, 2016
Short summary
Short summary
The paper presents the initial findings of the Arctic Snow Microstructure Experiment in Sodankylä, Finland. The experiment observed the microwave emission of extracted snow slabs on absorbing and reflecting bases. Snow parameters were recorded to simulate the emission upon those bases using two different emission models. The smallest simulation errors were associated with the absorbing base at vertical polarization. The observations will be used for the development of snow emission modelling.
J. I. Peltoniemi, M. Gritsevich, T. Hakala, P. Dagsson-Waldhauserová, Ó. Arnalds, K. Anttila, H.-R. Hannula, N. Kivekäs, H. Lihavainen, O. Meinander, J. Svensson, A. Virkkula, and G. de Leeuw
The Cryosphere, 9, 2323–2337, https://doi.org/10.5194/tc-9-2323-2015, https://doi.org/10.5194/tc-9-2323-2015, 2015
Short summary
Short summary
Light-absorbing impurities change the reflectance of snow in different ways. Some particles are heated by the Sun and they sink out of sight. During the process, snow may look darker than pure snow when observed by nadir, but at larger view zenith angles the snow may look as white as clean snow. Thus an observer on the ground may overestimate the albedo, while a satellite underestimates the albedo. Climate studies need to examine how the contaminants behave in snow, not only their total amounts.
J. Svensson, A. Virkkula, O. Meinander, N. Kivekäs, H.-R. Hannula, O. Järvinen, J. I. Peltoniemi, M. Gritsevich, A. Heikkilä, A. Kontu, A.-P. Hyvärinen, K. Neitola, D. Brus, P. Dagsson-Waldhauserova, K. Anttila, T. Hakala, H. Kaartinen, M. Vehkamäki, G. de Leeuw, and H. Lihavainen
The Cryosphere Discuss., https://doi.org/10.5194/tcd-9-1227-2015, https://doi.org/10.5194/tcd-9-1227-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
Soot's (including black carbon and organics) negative effect on a natural snow pack is experimentally addressed in this paper through a series of experiments. Soot concentrations in the snow in the range of 200-200 000 ppb verify the negative effects on the albedo, the physical snow characteristics, as well as increasing the melt rate of the snow pack. Our experimental data generally agrees when compared with the Snow, Ice and Aerosol Radiation model.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
Short summary
Short summary
Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
Short summary
Short summary
Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Chao Yan, Yicheng Shen, Dominik Stolzenburg, Lubna Dada, Ximeng Qi, Simo Hakala, Anu-Maija Sundström, Yishuo Guo, Antti Lipponen, Tom V. Kokkonen, Jenni Kontkanen, Runlong Cai, Jing Cai, Tommy Chan, Liangduo Chen, Biwu Chu, Chenjuan Deng, Wei Du, Xiaolong Fan, Xu-Cheng He, Juha Kangasluoma, Joni Kujansuu, Mona Kurppa, Chang Li, Yiran Li, Zhuohui Lin, Yiliang Liu, Yuliang Liu, Yiqun Lu, Wei Nie, Jouni Pulliainen, Xiaohui Qiao, Yonghong Wang, Yifan Wen, Ye Wu, Gan Yang, Lei Yao, Rujing Yin, Gen Zhang, Shaojun Zhang, Feixue Zheng, Ying Zhou, Antti Arola, Johanna Tamminen, Pauli Paasonen, Yele Sun, Lin Wang, Neil M. Donahue, Yongchun Liu, Federico Bianchi, Kaspar R. Daellenbach, Douglas R. Worsnop, Veli-Matti Kerminen, Tuukka Petäjä, Aijun Ding, Jingkun Jiang, and Markku Kulmala
Atmos. Chem. Phys., 22, 12207–12220, https://doi.org/10.5194/acp-22-12207-2022, https://doi.org/10.5194/acp-22-12207-2022, 2022
Short summary
Short summary
Atmospheric new particle formation (NPF) is a dominant source of atmospheric ultrafine particles. In urban environments, traffic emissions are a major source of primary pollutants, but their contribution to NPF remains under debate. During the COVID-19 lockdown, traffic emissions were significantly reduced, providing a unique chance to examine their relevance to NPF. Based on our comprehensive measurements, we demonstrate that traffic emissions alone are not able to explain the NPF in Beijing.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Bin Cheng, Yubing Cheng, Timo Vihma, Anna Kontu, Fei Zheng, Juha Lemmetyinen, Yubao Qiu, and Jouni Pulliainen
Earth Syst. Sci. Data, 13, 3967–3978, https://doi.org/10.5194/essd-13-3967-2021, https://doi.org/10.5194/essd-13-3967-2021, 2021
Short summary
Short summary
Climate change strongly impacts the Arctic, with clear signs of higher air temperature and more precipitation. A sustainable observation programme has been carried out in Lake Orajärvi in Sodankylä, Finland. The high-quality air–snow–ice–water temperature profiles have been measured every winter since 2009. The data can be used to investigate the lake ice surface heat balance and the role of snow in lake ice mass balance and parameterization of snow-to-ice transformation in snow/ice models.
Pinja Venäläinen, Kari Luojus, Juha Lemmetyinen, Jouni Pulliainen, Mikko Moisander, and Matias Takala
The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, https://doi.org/10.5194/tc-15-2969-2021, 2021
Short summary
Short summary
Information about snow water equivalent (SWE) is needed in many applications, including climate model evaluation and forecasting fresh water availability. Space-borne radiometer observations combined with ground snow depth measurements can be used to make global estimates of SWE. In this study, we investigate the possibility of using sparse snow density measurement in satellite-based SWE retrieval and show that using the snow density information in post-processing improves SWE estimations.
Terhikki Manninen, Tuula Aalto, Tiina Markkanen, Mikko Peltoniemi, Kristin Böttcher, Sari Metsämäki, Kati Anttila, Pentti Pirinen, Antti Leppänen, and Ali Nadir Arslan
Biogeosciences, 16, 223–240, https://doi.org/10.5194/bg-16-223-2019, https://doi.org/10.5194/bg-16-223-2019, 2019
Short summary
Short summary
The surface albedo time series CLARA-A2 SAL was used to study trends in the timing of the melting season of snow and preceding albedo value in Finland during 1982–2016 to assess climate change. The results were in line with operational snow depth data, JSBACH land ecosystem model, SYKE fractional snow cover and greening-up data. In the north a clear trend to earlier snowmelt onset, increasing melting season length, and decrease in pre-melt albedo (related to increased stem volume) was observed.
Jonas Svensson, Johan Ström, Niku Kivekäs, Nathaniel B. Dkhar, Shresth Tayal, Ved P. Sharma, Arttu Jutila, John Backman, Aki Virkkula, Meri Ruppel, Antti Hyvärinen, Anna Kontu, Henna-Reetta Hannula, Matti Leppäranta, Rakesh K. Hooda, Atte Korhola, Eija Asmi, and Heikki Lihavainen
Atmos. Meas. Tech., 11, 1403–1416, https://doi.org/10.5194/amt-11-1403-2018, https://doi.org/10.5194/amt-11-1403-2018, 2018
Short summary
Short summary
Receding glaciers in the Himalayas are of concern. Here we present measurements of light-absorbing impurities, known to contribute to the ongoing glacier decrease, in snow from Indian Himalayas and compare them to snow samples from the Finnish Arctic. The soot particles in the snow are shown to have lower light absorbing efficiency, possibly affecting their radiative forcing potential in the snow. Further, dust influences the snow in the Himalayas to a much greater extent than in Finland.
Mikko Peltoniemi, Mika Aurela, Kristin Böttcher, Pasi Kolari, John Loehr, Jouni Karhu, Maiju Linkosalmi, Cemal Melih Tanis, Juha-Pekka Tuovinen, and Ali Nadir Arslan
Earth Syst. Sci. Data, 10, 173–184, https://doi.org/10.5194/essd-10-173-2018, https://doi.org/10.5194/essd-10-173-2018, 2018
Short summary
Short summary
Monitoring ecosystems using low-cost time lapse cameras has gained wide interest among researchers worldwide. Quantitative information stored in image pixels can be analysed automatically to track time-dependent phenomena, e.g. seasonal course of leaves in the canopies or snow on ground. As such, cameras can provide valuable ground references to earth observation. Here we document the ecosystem camera network we established to Finland and publish time series of images recorded between 2014–2016.
Maiju Linkosalmi, Mika Aurela, Juha-Pekka Tuovinen, Mikko Peltoniemi, Cemal M. Tanis, Ali N. Arslan, Pasi Kolari, Kristin Böttcher, Tuula Aalto, Juuso Rainne, Juha Hatakka, and Tuomas Laurila
Geosci. Instrum. Method. Data Syst., 5, 417–426, https://doi.org/10.5194/gi-5-417-2016, https://doi.org/10.5194/gi-5-417-2016, 2016
Short summary
Short summary
Digital photography has become a widely used tool for monitoring the vegetation phenology. The seasonal cycle of the greenness index obtained from photographs correlated well with the CO2 exchange of the plants at our wetland and Scots pine forest sites. While the seasonal changes in the greenness were more obvious for the ecosystem dominated by annual plants, clear seasonal patterns were also observed for the evergreen forest.
Juha Lemmetyinen, Anna Kontu, Jouni Pulliainen, Juho Vehviläinen, Kimmo Rautiainen, Andreas Wiesmann, Christian Mätzler, Charles Werner, Helmut Rott, Thomas Nagler, Martin Schneebeli, Martin Proksch, Dirk Schüttemeyer, Michael Kern, and Malcolm W. J. Davidson
Geosci. Instrum. Method. Data Syst., 5, 403–415, https://doi.org/10.5194/gi-5-403-2016, https://doi.org/10.5194/gi-5-403-2016, 2016
Henna-Reetta Hannula, Juha Lemmetyinen, Anna Kontu, Chris Derksen, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 347–363, https://doi.org/10.5194/gi-5-347-2016, https://doi.org/10.5194/gi-5-347-2016, 2016
Short summary
Short summary
The paper described an extensive in situ data set of bulk snow depth, snow water equivalent, and snow density collected as a support of SnowSAR-2 airborne campaign in northern Finland. The spatial and temporal variability of these snow properties was analyzed in different land cover types. The success of the chosen measurement protocol to provide an accurate reference for the simultaneous SAR data products was analyzed in the context of spatial scale, sample size, and uncertainty.
Kaisa Lakkala, Hanne Suokanerva, Juha Matti Karhu, Antti Aarva, Antti Poikonen, Tomi Karppinen, Markku Ahponen, Henna-Reetta Hannula, Anna Kontu, and Esko Kyrö
Geosci. Instrum. Method. Data Syst., 5, 315–320, https://doi.org/10.5194/gi-5-315-2016, https://doi.org/10.5194/gi-5-315-2016, 2016
Short summary
Short summary
This paper describes the laboratory facilities at the Finnish Meteorological Institute – Arctic Research Centre (FMI-ARC). They comprise an optical laboratory, a facility for biological studies, and an office. The facilities are ideal for responding to the needs of international multidisciplinary research, giving the possibility to calibrate and characterize the research instruments as well as handle and store samples.
Leena Leppänen, Anna Kontu, Henna-Reetta Hannula, Heidi Sjöblom, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 163–179, https://doi.org/10.5194/gi-5-163-2016, https://doi.org/10.5194/gi-5-163-2016, 2016
Short summary
Short summary
The manual snow survey program of Finnish Meteorological Institute consists of numerous observations of natural seasonal snowpack in Sodankylä, in northern Finland. Systematic snow measurements began in 1911 with snow depth and snow water equivalent. In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from snow pits. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack.
Jaakko Ikonen, Juho Vehviläinen, Kimmo Rautiainen, Tuomo Smolander, Juha Lemmetyinen, Simone Bircher, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 95–108, https://doi.org/10.5194/gi-5-95-2016, https://doi.org/10.5194/gi-5-95-2016, 2016
Short summary
Short summary
A comprehensive, distributed network of in situ measurement stations gathering information on soil moisture has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network is used as a tool to evaluate the validity of satellite retrievals of soil properties. We present the soil moisture observation network and the results of comparisons of top layer soil moisture between 2012 and 2014 against ESA CCI product soil moisture retrievals.
William Maslanka, Leena Leppänen, Anna Kontu, Mel Sandells, Juha Lemmetyinen, Martin Schneebeli, Martin Proksch, Margret Matzl, Henna-Reetta Hannula, and Robert Gurney
Geosci. Instrum. Method. Data Syst., 5, 85–94, https://doi.org/10.5194/gi-5-85-2016, https://doi.org/10.5194/gi-5-85-2016, 2016
Short summary
Short summary
The paper presents the initial findings of the Arctic Snow Microstructure Experiment in Sodankylä, Finland. The experiment observed the microwave emission of extracted snow slabs on absorbing and reflecting bases. Snow parameters were recorded to simulate the emission upon those bases using two different emission models. The smallest simulation errors were associated with the absorbing base at vertical polarization. The observations will be used for the development of snow emission modelling.
J. I. Peltoniemi, M. Gritsevich, T. Hakala, P. Dagsson-Waldhauserová, Ó. Arnalds, K. Anttila, H.-R. Hannula, N. Kivekäs, H. Lihavainen, O. Meinander, J. Svensson, A. Virkkula, and G. de Leeuw
The Cryosphere, 9, 2323–2337, https://doi.org/10.5194/tc-9-2323-2015, https://doi.org/10.5194/tc-9-2323-2015, 2015
Short summary
Short summary
Light-absorbing impurities change the reflectance of snow in different ways. Some particles are heated by the Sun and they sink out of sight. During the process, snow may look darker than pure snow when observed by nadir, but at larger view zenith angles the snow may look as white as clean snow. Thus an observer on the ground may overestimate the albedo, while a satellite underestimates the albedo. Climate studies need to examine how the contaminants behave in snow, not only their total amounts.
E. Malnes, A. Buanes, T. Nagler, G. Bippus, D. Gustafsson, C. Schiller, S. Metsämäki, J. Pulliainen, K. Luojus, H. E. Larsen, R. Solberg, A. Diamandi, and A. Wiesmann
The Cryosphere, 9, 1191–1202, https://doi.org/10.5194/tc-9-1191-2015, https://doi.org/10.5194/tc-9-1191-2015, 2015
Short summary
Short summary
The paper provides detailed information on the outcome of a user survey carried out in the EU FP7 project CryoLand. The project focuses on monitoring of seasonal snow, glaciers and lake/river ice. The user survey showed that a European operational snow and land ice service is required and that there exists products that can meet the specific needs. The majority of the users were mainly interested in the snow services, but also the lake/river ice products and the glacier products were desired.
J. Svensson, A. Virkkula, O. Meinander, N. Kivekäs, H.-R. Hannula, O. Järvinen, J. I. Peltoniemi, M. Gritsevich, A. Heikkilä, A. Kontu, A.-P. Hyvärinen, K. Neitola, D. Brus, P. Dagsson-Waldhauserova, K. Anttila, T. Hakala, H. Kaartinen, M. Vehkamäki, G. de Leeuw, and H. Lihavainen
The Cryosphere Discuss., https://doi.org/10.5194/tcd-9-1227-2015, https://doi.org/10.5194/tcd-9-1227-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
Soot's (including black carbon and organics) negative effect on a natural snow pack is experimentally addressed in this paper through a series of experiments. Soot concentrations in the snow in the range of 200-200 000 ppb verify the negative effects on the albedo, the physical snow characteristics, as well as increasing the melt rate of the snow pack. Our experimental data generally agrees when compared with the Snow, Ice and Aerosol Radiation model.
P. Räisänen, A. Luomaranta, H. Järvinen, M. Takala, K. Jylhä, O. N. Bulygina, K. Luojus, A. Riihelä, A. Laaksonen, J. Koskinen, and J. Pulliainen
Geosci. Model Dev., 7, 3037–3057, https://doi.org/10.5194/gmd-7-3037-2014, https://doi.org/10.5194/gmd-7-3037-2014, 2014
Short summary
Short summary
Snowmelt influences greatly the climatic conditions in spring. This study evaluates the timing of springtime end of snowmelt in the ECHAM5 model. A key finding is that, in much of northern Eurasia, snow disappears too early in ECHAM5, in spite of a slight cold bias in spring. This points to the need for a more comprehensive treatment of the surface energy budget. In particular, the surface temperature for the snow-covered and snow-free parts of a climate model grid cell should be separated.
Related subject area
Snow and Sea Ice
Time series of alpine snow surface radiative-temperature maps from high-precision thermal-infrared imaging
Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023
SMOS-derived Antarctic thin sea ice thickness: data description and validation in the Weddell Sea
A 12-year climate record of wintertime wave-affected marginal ice zones in the Atlantic Arctic based on CryoSat-2
MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022)
Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977
A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF)
Snow accumulation and ablation measurements in a midlatitude mountain coniferous forest (Col de Porte, France, 1325 m altitude): the Snow Under Forest (SnoUF) field campaign data set
A new sea ice concentration product in the polar regions derived from the FengYun-3 MWRI sensors
NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in situ snow depth time series
IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)
HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model
Large ensemble of downscaled historical daily snowfall from an earth system model to 5.5 km resolution over Dronning Maud Land, Antarctica
The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021)
A new Greenland digital elevation model derived from ICESat-2 during 2018–2019
Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach
Snow depth product over Antarctic sea ice from 2002 to 2020 using multisource passive microwave radiometers
The NIEER AVHRR snow cover extent product over China – a long-term daily snow record for regional climate research
Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)
Meteorological, snow and soil data (2013–2019) from a herb tundra permafrost site at Bylot Island, Canadian high Arctic, for driving and testing snow and land surface models
Inter-annual variation in lake ice composition in the European Arctic: observations based on high-resolution thermistor strings
Daily Terra–Aqua MODIS cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019
High-resolution mapping of circum-Antarctic landfast sea ice distribution, 2000–2018
An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018
Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China
Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data
Hydrometeorological data from Marmot Creek Research Basin, Canadian Rockies
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data, 16, 3913–3934, https://doi.org/10.5194/essd-16-3913-2024, https://doi.org/10.5194/essd-16-3913-2024, 2024
Short summary
Short summary
High-accuracy precision maps of the surface temperature of snow were acquired with an uncooled thermal-infrared camera during winter 2021–2022 and spring 2023. The accuracy – i.e., mean absolute error – improved from 1.28 K to 0.67 K between the seasons thanks to an improved camera setup and temperature stabilization. The dataset represents a major advance in the validation of satellite measurements and physical snow models over a complex topography.
Michael Warscher, Thomas Marke, Erwin Rottler, and Ulrich Strasser
Earth Syst. Sci. Data, 16, 3579–3599, https://doi.org/10.5194/essd-16-3579-2024, https://doi.org/10.5194/essd-16-3579-2024, 2024
Short summary
Short summary
Continuous observations of snow and climate at high altitudes are still sparse. We present a unique collection of weather and snow cover data from three automatic weather stations at remote locations in the Ötztal Alps (Austria) that include continuous recordings of snow cover properties. The data are available over multiple winter seasons and enable new insights for snow hydrological research. The data are also used in operational applications, i.e., for avalanche warning and flood forecasting.
Lars Kaleschke, Xiangshan Tian-Kunze, Stefan Hendricks, and Robert Ricker
Earth Syst. Sci. Data, 16, 3149–3170, https://doi.org/10.5194/essd-16-3149-2024, https://doi.org/10.5194/essd-16-3149-2024, 2024
Short summary
Short summary
We describe a sea ice thickness dataset based on SMOS satellite measurements, initially designed for the Arctic but adapted for Antarctica. We validated it using limited Antarctic measurements. Our findings show promising results, with a small difference in thickness estimation and a strong correlation with validation data within the valid thickness range. However, improvements and synergies with other sensors are needed, especially for sea ice thicker than 1 m.
Weixin Zhu, Siqi Liu, Shiming Xu, and Lu Zhou
Earth Syst. Sci. Data, 16, 2917–2940, https://doi.org/10.5194/essd-16-2917-2024, https://doi.org/10.5194/essd-16-2917-2024, 2024
Short summary
Short summary
In the polar ocean, wind waves generate and propagate into the sea ice cover, forming marginal ice zones (MIZs). Using ESA's CryoSat-2, we construct a 12-year dataset of the MIZ in the Atlantic Arctic, a key region for climate change and human activities. The dataset is validated with high-resolution observations by ICESat2 and Sentinel-1. MIZs over 300 km wide are found under storms in the Barents Sea. The new dataset serves as the basis for research areas, including wave–ice interactions.
Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jianwei Yang, Zhaojun Zheng, Shengli Wu, and Jiancheng Shi
Earth Syst. Sci. Data, 16, 2501–2523, https://doi.org/10.5194/essd-16-2501-2024, https://doi.org/10.5194/essd-16-2501-2024, 2024
Short summary
Short summary
It is important to strengthen the continuous monitoring of snow cover as a key indicator of imbalance in the Asian Water Tower (AWT) region. We generate long-term daily gap-free fractional snow cover products over the AWT at 0.005° resolution from 2000 to 2022 based on the multiple-endmember spectral mixture analysis algorithm and the gap-filling algorithm. They can provide highly accurate, quantitative fractional snow cover information for subsequent studies on hydrology and climate.
Wiebke Margitta Kolbe, Rasmus T. Tonboe, and Julienne Stroeve
Earth Syst. Sci. Data, 16, 1247–1264, https://doi.org/10.5194/essd-16-1247-2024, https://doi.org/10.5194/essd-16-1247-2024, 2024
Short summary
Short summary
Current satellite-based sea-ice climate data records (CDRs) usually begin in October 1978 with the first multichannel microwave radiometer data. Here, we present a sea ice dataset based on the single-channel Electrical Scanning Microwave Radiometer (ESMR) that operated from 1972-1977 onboard NASA’s Nimbus 5 satellite. The data were processed using modern methods and include uncertainty estimations in order to provide an important, easy-to-use reference period of good quality for current CDRs.
Thomas Lavergne and Emily Down
Earth Syst. Sci. Data, 15, 5807–5834, https://doi.org/10.5194/essd-15-5807-2023, https://doi.org/10.5194/essd-15-5807-2023, 2023
Short summary
Short summary
Sea ice in the Arctic and Antarctic can move several tens of kilometers per day due to wind and ocean currents. By analysing thousands of satellite images, we measured how sea ice has been moving every single day from 1991 through to 2020. We compare our data to how buoys attached to the ice moved and find good agreement. Other scientists will now use our data to better understand if climate change has modified the way sea ice moves and in what way.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
Short summary
Short summary
Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Ying Chen, Ruibo Lei, Xi Zhao, Shengli Wu, Yue Liu, Pei Fan, Qing Ji, Peng Zhang, and Xiaoping Pang
Earth Syst. Sci. Data, 15, 3223–3242, https://doi.org/10.5194/essd-15-3223-2023, https://doi.org/10.5194/essd-15-3223-2023, 2023
Short summary
Short summary
The sea ice concentration product derived from the Microwave Radiation Image sensors on board the FengYun-3 satellites can reasonably and independently identify the seasonal and long-term changes of sea ice, as well as extreme cases of annual maximum and minimum sea ice extent in polar regions. It is comparable with other sea ice concentration products and applied to the studies of climate and marine environment.
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen
Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023, https://doi.org/10.5194/essd-15-2577-2023, 2023
Short summary
Short summary
We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11 071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Flavio Pignone, Giulia Bruno, Luca Pulvirenti, Giuseppe Squicciarino, Elisabetta Fiori, Lauro Rossi, Silvia Puca, Alexander Toniazzo, Pietro Giordano, Marco Falzacappa, Sara Ratto, Hervè Stevenin, Antonio Cardillo, Matteo Fioletti, Orietta Cazzuli, Edoardo Cremonese, Umberto Morra di Cella, and Luca Ferraris
Earth Syst. Sci. Data, 15, 639–660, https://doi.org/10.5194/essd-15-639-2023, https://doi.org/10.5194/essd-15-639-2023, 2023
Short summary
Short summary
Snow cover has profound implications for worldwide water supply and security, but knowledge of its amount and distribution across the landscape is still elusive. We present IT-SNOW, a reanalysis comprising daily maps of snow amount and distribution across Italy for 11 snow seasons from September 2010 to August 2021. The reanalysis was validated using satellite images and snow measurements and will provide highly needed data to manage snow water resources in a warming climate.
Yan Huang, Jiahui Xu, Jingyi Xu, Yelei Zhao, Bailang Yu, Hongxing Liu, Shujie Wang, Wanjia Xu, Jianping Wu, and Zhaojun Zheng
Earth Syst. Sci. Data, 14, 4445–4462, https://doi.org/10.5194/essd-14-4445-2022, https://doi.org/10.5194/essd-14-4445-2022, 2022
Short summary
Short summary
Reliable snow cover information is important for understating climate change and hydrological cycling. We generate long-term daily gap-free snow products over the Tibetan Plateau (TP) at 500 m resolution from 2002 to 2021 based on the hidden Markov random field model. The accuracy is 91.36 %, and is especially improved during snow transitional period and over complex terrains. This dataset has great potential to study climate change and to facilitate water resource management in the TP.
Nicolas Ghilain, Stéphane Vannitsem, Quentin Dalaiden, Hugues Goosse, Lesley De Cruz, and Wenguang Wei
Earth Syst. Sci. Data, 14, 1901–1916, https://doi.org/10.5194/essd-14-1901-2022, https://doi.org/10.5194/essd-14-1901-2022, 2022
Short summary
Short summary
Modeling the climate at high resolution is crucial to represent the snowfall accumulation over the complex orography of the Antarctic coast. While ice cores provide a view constrained spatially but over centuries, climate models can give insight into its spatial distribution, either at high resolution over a short period or vice versa. We downscaled snowfall accumulation from climate model historical simulations (1850–present day) over Dronning Maud Land at 5.5 km using a statistical method.
Matthieu Vernay, Matthieu Lafaysse, Diego Monteiro, Pascal Hagenmuller, Rafife Nheili, Raphaëlle Samacoïts, Deborah Verfaillie, and Samuel Morin
Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, https://doi.org/10.5194/essd-14-1707-2022, 2022
Short summary
Short summary
This paper introduces the latest version of the freely available S2M dataset which provides estimates of both meteorological and snow cover variables, as well as various avalanche hazard diagnostics at different elevations, slopes and aspects for the three main French high-elevation mountainous regions. A complete description of the system and the dataset is provided, as well as an overview of the possible uses of this dataset and an objective assessment of its limitations.
Yubin Fan, Chang-Qing Ke, and Xiaoyi Shen
Earth Syst. Sci. Data, 14, 781–794, https://doi.org/10.5194/essd-14-781-2022, https://doi.org/10.5194/essd-14-781-2022, 2022
Short summary
Short summary
A new digital elevation model of Greenland was provided based on the ICESat-2 observations acquired from November 2018 to November 2019. A model fit method was applied within the grid cells at different spatial resolutions to estimate the surface elevations with a modal resolution of 500 m. We estimated the uncertainty with a median difference of −0.48 m for all of Greenland, which can benefit studies of elevation change and mass balance in Greenland.
Donghang Shao, Hongyi Li, Jian Wang, Xiaohua Hao, Tao Che, and Wenzheng Ji
Earth Syst. Sci. Data, 14, 795–809, https://doi.org/10.5194/essd-14-795-2022, https://doi.org/10.5194/essd-14-795-2022, 2022
Short summary
Short summary
The temporal series and spatial distribution discontinuity of the existing snow water equivalent (SWE) products in the pan-Arctic region severely restricts the use of SWE data in cryosphere change and climate change studies. Using a ridge regression machine learning algorithm, this study developed a set of spatiotemporally seamless and high-precision SWE products. This product could contribute to the study of cryosphere change and climate change at large spatial scales.
Xiaoyi Shen, Chang-Qing Ke, and Haili Li
Earth Syst. Sci. Data, 14, 619–636, https://doi.org/10.5194/essd-14-619-2022, https://doi.org/10.5194/essd-14-619-2022, 2022
Short summary
Short summary
Snow over Antarctic sea ice controls energy budgets and thus has essential effects on the climate. Here, we estimated snow depth using microwave radiometers and a newly constructed, robust method by incorporating lower frequencies, which have been available from AMSR-E and AMSR-2. Comparing the new retrieval with in situ and shipborne snow depth measurements showed that this method outperformed the previously available method.
Xiaohua Hao, Guanghui Huang, Tao Che, Wenzheng Ji, Xingliang Sun, Qin Zhao, Hongyu Zhao, Jian Wang, Hongyi Li, and Qian Yang
Earth Syst. Sci. Data, 13, 4711–4726, https://doi.org/10.5194/essd-13-4711-2021, https://doi.org/10.5194/essd-13-4711-2021, 2021
Short summary
Short summary
Long-term snow cover data are not only of importance for climate research. Currently China still lacks a high-quality snow cover extent (SCE) product for climate research. This study develops a multi-level decision tree algorithm for cloud and snow discrimination and gap-filled technique based on AVHRR surface reflectance data. We generate a daily 5 km SCE product across China from 1981 to 2019. It has high accuracy and will serve as baseline data for climate and other applications.
Vincent Vionnet, Colleen Mortimer, Mike Brady, Louise Arnal, and Ross Brown
Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, https://doi.org/10.5194/essd-13-4603-2021, 2021
Short summary
Short summary
Water equivalent of snow cover (SWE) is a key variable for water management, hydrological forecasting and climate monitoring. A new Canadian SWE dataset (CanSWE) is presented in this paper. It compiles data collected by multiple agencies and companies at more than 2500 different locations across Canada over the period 1928–2020. Snow depth and derived bulk snow density are also included when available.
Florent Domine, Georg Lackner, Denis Sarrazin, Mathilde Poirier, and Maria Belke-Brea
Earth Syst. Sci. Data, 13, 4331–4348, https://doi.org/10.5194/essd-13-4331-2021, https://doi.org/10.5194/essd-13-4331-2021, 2021
Short summary
Short summary
Current sophisticated snow physics models were mostly designed for alpine conditions and cannot adequately simulate the physical properties of Arctic snowpacks. New snow models will require Arctic data sets for forcing and validation. We provide an extensive driving and testing data set from a high Arctic herb tundra site in Canada. Unique validating data include continuous time series of snow and soil thermal conductivity and temperature profiles. Field observations in spring are provided.
Bin Cheng, Yubing Cheng, Timo Vihma, Anna Kontu, Fei Zheng, Juha Lemmetyinen, Yubao Qiu, and Jouni Pulliainen
Earth Syst. Sci. Data, 13, 3967–3978, https://doi.org/10.5194/essd-13-3967-2021, https://doi.org/10.5194/essd-13-3967-2021, 2021
Short summary
Short summary
Climate change strongly impacts the Arctic, with clear signs of higher air temperature and more precipitation. A sustainable observation programme has been carried out in Lake Orajärvi in Sodankylä, Finland. The high-quality air–snow–ice–water temperature profiles have been measured every winter since 2009. The data can be used to investigate the lake ice surface heat balance and the role of snow in lake ice mass balance and parameterization of snow-to-ice transformation in snow/ice models.
Sher Muhammad and Amrit Thapa
Earth Syst. Sci. Data, 13, 767–776, https://doi.org/10.5194/essd-13-767-2021, https://doi.org/10.5194/essd-13-767-2021, 2021
Short summary
Short summary
Snow is a dominant water resource in high-mountain Asia and crucial for mountain communities and downstream populations. The present MODIS snow products are significantly uncertain and not useful for observation and simulation of climate, hydrology, and other water-related studies. This study reduces uncertainty in the daily MODIS snow data and generates a MODIS Terra–Aqua combined product reducing uncertainties due to cloud cover, data gaps, and other errors caused by sensor limitations.
Alexander D. Fraser, Robert A. Massom, Kay I. Ohshima, Sascha Willmes, Peter J. Kappes, Jessica Cartwright, and Richard Porter-Smith
Earth Syst. Sci. Data, 12, 2987–2999, https://doi.org/10.5194/essd-12-2987-2020, https://doi.org/10.5194/essd-12-2987-2020, 2020
Short summary
Short summary
Landfast ice, or
fast ice, is a form of sea ice which is mechanically fastened to stationary parts of the coast. Long-term and accurate knowledge of its extent around Antarctica is critical for understanding a number of important Antarctic coastal processes, yet no accurate, large-scale, long-term dataset of its extent has been available. We address this data gap with this new dataset compiled from satellite imagery, containing high-resolution maps of Antarctic fast ice from 2000 to 2018.
Sher Muhammad and Amrit Thapa
Earth Syst. Sci. Data, 12, 345–356, https://doi.org/10.5194/essd-12-345-2020, https://doi.org/10.5194/essd-12-345-2020, 2020
Short summary
Short summary
Snow is the major water resource in high-mountain Asia; therefore, it is crucial to continuously monitor it. Currently, remote sensing, mainly MODIS, is used for snow monitoring. However, the available MODIS snow product is not useful for various applications without postprocessing and improvement. This study reduces uncertainty in the MODIS snow data. We found approximately 50% underestimation and overestimation of snow cover by MODIS Terra–Aqua products, which were improved in this study.
Tao Che, Xin Li, Shaomin Liu, Hongyi Li, Ziwei Xu, Junlei Tan, Yang Zhang, Zhiguo Ren, Lin Xiao, Jie Deng, Rui Jin, Mingguo Ma, Jian Wang, and Xiaofan Yang
Earth Syst. Sci. Data, 11, 1483–1499, https://doi.org/10.5194/essd-11-1483-2019, https://doi.org/10.5194/essd-11-1483-2019, 2019
Short summary
Short summary
The paper presents a suite of datasets consisting of long-term hydrometeorological, snow cover and frozen ground data for investigating watershed science and functions from an integrated, distributed and multiscale observation network in the upper reaches of the Heihe River Basin in China. These data are expected to serve as a testing platform to provide accurate forcing data and validate and evaluate remote-sensing products and hydrological models in cold regions for a broader community.
Cécile B. Ménard, Richard Essery, Alan Barr, Paul Bartlett, Jeff Derry, Marie Dumont, Charles Fierz, Hyungjun Kim, Anna Kontu, Yves Lejeune, Danny Marks, Masashi Niwano, Mark Raleigh, Libo Wang, and Nander Wever
Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, https://doi.org/10.5194/essd-11-865-2019, 2019
Short summary
Short summary
This paper describes long-term meteorological and evaluation datasets from 10 reference sites for use in snow modelling. We demonstrate how data sharing is crucial to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The ease of use, availability, and quality of the datasets will help model developers quantify and reduce model uncertainties and errors.
Xing Fang, John W. Pomeroy, Chris M. DeBeer, Phillip Harder, and Evan Siemens
Earth Syst. Sci. Data, 11, 455–471, https://doi.org/10.5194/essd-11-455-2019, https://doi.org/10.5194/essd-11-455-2019, 2019
Short summary
Short summary
Meteorological, snow survey, streamflow, and groundwater data are presented from Marmot Creek Research Basin, a small alpine-montane forest headwater catchment in the Alberta Rockies. It was heavily instrumented, experimented upon, and operated by several federal government agencies between 1962 and 1986 and was re-established starting in 2004 by the University of Saskatchewan Centre for Hydrology. These long-term legacy data serve to advance our knowledge of hydrology of the Canadian Rockies.
Cited articles
ACIA: Arctic Climate Impact Assessment, ACIA Overview report, Cambridge
University Press, 1020 pp., 2005.
AMAP: Snow, Water, Ice and Permafrost in the Arctic (SWIPA), Arctic
Monitoring and Assessment Programme (AMAP), Oslo, Norway. xiv + 269 pp.,
available at: https://www.amap.no/documents/doc/snow-water-ice-and-permafrost-in-the-arctic-swipa-2017/1610 (last access: December 2019),
2017.
Anderson, K., Dungan, J. L., and MacArthur, A.: On the reproducibility of
field-measured reflectance factors in the context of vegetation studies,
Remote Sens. Environ., 115, 1893–1905, 2011.
Aoki, T., Aoki, T., Fukabori, M., Hachikubo, A., Tachibana, Y., and Nishio,
F.: Effects of snow physical parameters on spectral albedo and bidirectional
reflectance of snow surface, J. Geophys. Res., 105, 10219–10236, 2000.
Bänninger, D., Bourgeois, C. S., Matzl, M., and Schneebeli, M.:
Reflectance Modeling for Real Snow Structures Using a Beam Tracing Model,
Sensors, 8, 3482–3496, 2008.
Böttcher, K., Aurela, M., Kervinen, M., Markkanen, T., Mattila, O.-P.,
Kolari, P., Metsämäki, S., Aalto, T., Arslan, A. N., and Pulliainen,
J.: MODIS time-series-derived indicators for the beginning of the growing
season in boreal coniferous forest – A comparison with CO2 flux
measurements and phenological observations in Finland, Remote Sens.
Environ., 140, 625–638, 2014.
Brown, R. D. and Mote, P. W.: The response of Northern Hemisphere snow cover
to a changing climate, J. Climate, 22, 2124–2144, 2009.
Brown, R. D. and Robinson, D. A.: Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty, The Cryosphere, 5, 219–229, https://doi.org/10.5194/tc-5-219-2011, 2011.
Callaghan, T. V., Johansson, M., Brown, R. D., Groisman, P. Y., Labba, N.,
Radionov, V., Bradley, R. S., Blangy, S., Bulygina, O. N., Christensen, T. R.,
Colman, J. E., Essery, R. L. H., Forbes, B. C., Forchhammer, M. C., Golubev,
V. N., Honrath, R. E., Juday, G. P., Meshcherskaya, A. V., Phoenix, G. K.,
Pomeroy, J., Rautio, A., Robinson, D. A., Schmidt, N. M., Serreze, M. C.,
Shevchenko, V. P., Shiklomanov, A. I., Shmakin, A. B., Sköld, P., Sturm,
M., Woo, M.-K., and Wood, E. F.: Multiple Effects of Changes in Arctic Snow
Cover, Ambio, 40, 32–45, 2011.
Choi, G., Robinson, D. A., and Kang, S.: Changing Northern Hemisphere snow
seasons, J. Climate, 23, 5305–5310, 2010.
Colbeck, S. C., Akitaya, E., Armstrong, R. L., Gubler, H., Lafeuille, J.,
Lied, K., McClung, D. M., and Morris, E. M.: The International
Classification for Seasonal Snow on the Ground, International Commission on
Snow and Ice (IAHS), World Data Center A for Glaciology, University of
Colorado, Boulder, CO, USA, 1990.
Derksen, C. and Brown, R.: Spring snow cover extent reductions in the
2008–2012 period exceeding climate model predictions. Geophys. Res. Lett.,
39, L19504, https://doi.org/10.1029/2012GL053387, 2012.
Dietz, A. J., Kuenzer, C., Gessner, U., and Dech, S.: Remote sensing of snow
– a review of available methods, Int. J. Remote Sens., 33, 4094–4134,
2012.
Dor, E. B., Ong, C., and Lau, I. C.: Reflectance measurements of soils in
the laboratory: Standards and protocols, Geoderma, 245, 112–124, 2015.
Dozier, J., Green, R. O., Nolin, A. W., and Painter, T. H.: Interpretation of
snow properties from imaging spectrometry, Remote Sens. Environ., 113,
25–27, 2009.
Essery, R., Kontu, A., Lemmetyinen, J., Dumont, M., and Ménard, C. B.: A 7-year dataset for driving and evaluating snow models at an Arctic site (Sodankylä, Finland), Geosci. Instrum. Method. Data Syst., 5, 219–227, https://doi.org/10.5194/gi-5-219-2016, 2016.
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., McClung,
D. M., Nishimura, K., Satyawali, P. K., and Sokratov, S. A.: The
International Classification for Seasonal Snow on the Ground, IHP-VII
Technical Documents in Hydrology No. 83, IACS Contribution No. 1, UNESCO-IHP,
Paris, 2009.
Frei, A., Tedesco, M., Lee, S., Foster, J., Hall, D. K., Kelly, R., and
Robinson, D. A.: A review of global satellite-derived snow products, Adv.
Space Res., 50, 1007–1029, 2012.
Gallet, J.-C., Domine, F., Zender, C. S., and Picard, G.: Measurement of the specific surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 nm, The Cryosphere, 3, 167–182, https://doi.org/10.5194/tc-3-167-2009, 2009.
Goetz, A. F.: Making accurate field spectral reflectance measurements, ASD
Inc., Boulder, CO, USA, 2012.
Hall, D. K. and Riggs, G. A.: Accuracy assessment of the MODIS snow products,
Hydrol. Process., 21, 1534–1547, 2007.
Hannula, H.-R. and Heinilä, K.: Laboratory spectral reflectance
measurements of pine/spruce twigs and snow, Zenodo,
https://doi.org/10.5281/zenodo.3580078, 2018a.
Hannula, H.-R. and Heinilä, K.: Mast-borne spectral reflectance
measurements of boreal landscape during spring, Zenodo,
https://doi.org/10.5281/zenodo.3580096, 2018b.
Hannula, H.-R. and Pulliainen, J.: Spectral reflectance behaviour of
different boreal snow types, J. Glaciol., 65, 926–939, 2019.
Hannula, H.-R., Heinilä, K., Böttcher, K., Mattila, O.-P., Salminen,
M., and Pulliainen, J.: Laboratory, field, mast-borne and airborne spectral
reflectance measurements of boreal landscape during spring, Zenodo, available at:
https://www.zenodo.org/communities/boreal_reflectances/?page=1&size=20, last access: December 2019.
Heinilä, K.: Airborne spectrometer measurements from boreal snow-covered
landscape, Zenodo, https://doi.org/10.5281/zenodo.3580451, 2019a.
Heinilä, K.: Airborne spectrometer measurements from boreal and tundra
site during spring snow melt, Zenodo, https://doi.org/10.5281/zenodo.3580419,
2019b.
Heinilä, K., Salminen M., Pulliainen J., Cohen J., Metsämäki S.,
and Pellikka P.: The effect of boreal forest canopy to reflectance of snow
covered terrain based on airborne imaging spectrometer observations, Int. J.
Appl. Earth Obs., 27, 31–41, 2014.
Heinilä, K., Böttcher, K., and Mattila, O.-P.: Spectrometer
measurements of snow and bare ground targets and simultaneous measurements
of snow conditions, Zenodo, https://doi.org/10.5281/zenodo.3580825, 2019a.
Heinilä, K., Salminen, M., Metsämäki, S., Pellikka, P., Koponen,
S., and Pulliainen, J.: Reflectance variation in boreal landscape during the
snow melting period using airborne imaging spectroscopy, Int. J. Appl. Earth
Obs., 76, 66–76, 2019b.
Hori, M., Sugiura, K., Kobayashi, K., Aoki, T., Tanikawa, T., Kuchiki, K.,
Niwano, M., and Enomoto, H.: A 38-year (1978–2015) Northern Hemisphere
daily snow cover extent product derived using consistent objective criteria
from satellite-borne optical sensors, Remote Sens. Environ., 191, 402–418,
2017.
Horton, S. and Jamieson, B.: Spectral measurements of surface hoar
crystals, J. Glaciol., 63, 477–486, 2017.
Hueni, A. and Bialek, A.: Cause, effect, and correction of field
spectroradiometer interchannel radiometric steps, IEEE J. Sel. Top. Appl.,
10, 1542–1551, 2017.
Hueni, A., Damm, A., Kneubuehler, M., Schläpfer, D., and Schaepman, M.
E.: Field and airborne spectroscopy cross validation – some considerations,
IEEE J. Sel Top. Appl., 10, 1117–1135, 2017.
Kangas, M., Rontu, L., Fortelius, C., Aurela, M., and Poikonen, A.: Weather model verification using Sodankylä mast measurements, Geosci. Instrum. Method. Data Syst., 5, 75–84, https://doi.org/10.5194/gi-5-75-2016, 2016.
Kimes, D. S., Kirchner, J. A., and Newcomb, W. W.: Spectral radiance errors
in remote sensing ground studies due to nearby objects, Appl. Optics, 22,
8–10, 1983.
Klein, A. G., Hall, D. K., and Riggs, G. A.: Improving snow-cover mapping in
forests through the use of a canopy reflectance model, Hydrol. Process., 12,
1723–1744, 1998.
Kokaly, R. F., Clark, R. N., Swayze, G. A., Livo, K. E., Hoefen, T. M., Pearson,
N. C., Wise, R. A., Benzel, W. M., Lowers, H. A., Driscoll, R. L., and Klein,
A. J.: USGS Spectral Library Version 7: U.S. Geological Survey Data Series
1035, 61 pp., https://doi.org/10.3133/ds1035, 2017.
Kriebel, K. T.: On the variability of the reflected radiation field due to
differing distributions of the irradiation, Remote Sens. Environ., 4,
257–264, 1976.
Leppänen, L., Kontu, A., Hannula, H.-R., Sjöblom, H., and Pulliainen, J.: Sodankylä manual snow survey program, Geosci. Instrum. Method. Data Syst., 5, 163–179, https://doi.org/10.5194/gi-5-163-2016, 2016.
Mac Arthur, A. and Robinson, I.: A critique of field spectroscopy and the
challenges and opportunities it presents for remote sensing for agriculture,
ecosystems, and hydrology, in: Proc. SPIE 9637, Remote Sensing for
Agriculture, Ecosystems, and Hydrology XVII, Toulouse, France,
1–11, 14 October 2015.
Mac Arthur A., MacLellan, C. J., and Malthus, T.: The fields of view and
directional response functions of two field spectroradiometers, IEEE T.
Geosci. Remote., 50, 3892–3907, 2012.
Ménard, C. B., Essery, R., Barr, A., Bartlett, P., Derry, J., Dumont, M., Fierz, C., Kim, H., Kontu, A., Lejeune, Y., Marks, D., Niwano, M., Raleigh, M., Wang, L., and Wever, N.: Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data, Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, 2019.
Metsämäki, S., Anttila, S., Huttunen, J., and Vepsäläinen, J.: A
feasible method for fractional snow cover mapping in boreal zone based on a
reflectance model, Remote Sens. Environ., 95, 77–95, 2005.
Metsämäki, S., Mattila, O.-P., Pulliainen, J., Niemi, K., Luojus, K., and
Böttcher, K.: An optical reflectance model-based method for fractional
snow cover mapping applicable to continental scale, Remote Sens. Environ.,
123, 508–521, 2012.
Metsämäki, S., Pulliainen, J., Salminen, M., Luojus, K., Wiesmann,
A., Solberg, R., Böttcher, K., Hiltunen, M., and Ripper, E.:
Introduction to GlobSnow Snow Extent products with considerations for
accuracy assessment, Remote Sens. Environ. 156, 96–108, 2015.
Milton, E., Schaepman, M., Anderson, K., Kneubühler, M., and Fox, N.:
Progress in field spectroscopy, Remote Sens. Environ., 113, S92–S109, 2009.
Niemi, K., Metsämäki, S., Pulliainen, J., Suokanerva, H., Böttcher,
K., Leppäranta, M., and Pellikka, P.: The behaviour of mast-borne spectra
in a snow-covered boreal forest, Remote Sens. Environ., 124, 551–563, 2012.
Nolin, A. W.: Recent advances in remote sensing of seasonal snow, J. Glaciol.,
56, 1141–1150, 2010.
Olson, D. M. and Dinerstein, E.: The Global 200: Priority ecoregions for
global conservation, Ann. Missouri Bot. Gard., 89, 199–224, 2002.
Otterman, J. and Fraser, R.: Adjacency effects on imaging by surface
reflection and atmospheric scattering: cross radiance to zenith, Appl.
Optics, 18, 2852–60, 1979.
Painter, T. H., Seidel, F., Bryant, A., Skiles, S. M., and Rittger, K.:
Imaging spectroscopy of albedo and radiative forcing by light-absorbing
impurities in mountain snow, J. Geophys. Res-Atmos., 118, 9511–9523, 2013.
Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A.,
Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., Ciais, P.,
Jackson, R. B., Pacala, S. W., McGuire, A. D., Piao, S., Rautiainen, A.,
Sitch, S., and Hayes, D.: A large and persistent carbon sink in the world's
forests, Science, 333, 988–993, 2011.
Peltoniemi, J. I., Kaasalainen, S., Näränen, J., Rautiainen, M.,
Stenberg, P., Smolander, H., Smolander, S., and Voipio, P.: BRDF measurement
of understory vegetation in pine forests: dwarf shrubs, lichen, and moss,
Remote Sens. Environ., 94, 343–354, 2005.
Pfitzner, K., Bartolo, R., Carr, G., Esparon, A., and Bollhöfer, A.: Standards
for reflectance spectral measurement of temporal vegetation plots,
Supervising Scientist Report 195, Supervising Scientist, Darwin NT, 2011.
Pirazzini, R., Räisänen, P., Vihma, T., Johansson, M., and Tastula, E.-M.: Measurements and modelling of snow particle size and shortwave infrared albedo over a melting Antarctic ice sheet, The Cryosphere, 9, 2357–2381, https://doi.org/10.5194/tc-9-2357-2015, 2015.
Pirinen, P., Simola, H., Aalto, J., Kaukoranta, J.-P., Karlsson, P., and
Ruuhela, R.: Climatological statistics of Finland 1981–2010, Finnish Meteorological Institute reports, 2012.
Pulliainen, J., Salminen, M., Heinilä, K., Cohen J., and Hannula H.-R.:
Semi-empirical modelling of the scene reflectance of snow-covered boreal
forest, Remote Sens. Environ., 155, 303–311, 2014.
Pulliainen, J., Aurela, M., Laurila, T., Aalto, T., Takala, M., Salminen,
M., Kulmala, M., Barr, A., Heimann, M., Lindroth, A., Laaksonen, A.,
Derksen, C., Mäkelä, A., Markkanen, T., Lemmetyinen, J., Susiluoto,
J., Dengel, S., Mammarella, I., Tuovinen, J.-P., and Vesala, T.: Early
snowmelt significantly enhances boreal springtime carbon uptake, P. Natl.
Acad. Sci. USA, 114, 11081–11086, https://doi.org/10.1073/pnas.1707889114,
2017.
Rasaiah, B. A., Jones, S. D., Bellman, C., and Malthus, T. J.: Critical
metadata for spectroscopy field campaign, Remote Sens. Environ., 6,
3662–3680, 2014.
Rasaiah, B. A., Jones, S. D., Bellman, C., Malthus, T. J., and Hueni, A.:
Assessing Field Spectroscopy Metadata Quality, Remote Sens., 7, 4499–4526,
2015.
Rollin, E., Milton, E., and Emery, D.: Reference panel anisotropy and
diffuse radiation – some implications for field spectroscopy, Int. J.
Remote Sens., 21, 2799–2810, 2000.
Román, M. O., Schaaf, C. B., Woodcock, C. E., Strahler, A. H., Yang, X.,
Braswell, R. H., Curtis, P. S., Davis, K. J., Dragoni, D., Goulden, M. L., Gu,
L., Hollinger, D. Y., Kolb, T. E., Meyers, T. P., Munger, J. W., Privette, J. L.,
Richardson, A. D., Wilson, T. B., and Wofsy, S. C.: Spatial scaling of
reflectance and surface albedo over a mixed-use, temperate forest landscape
during snow-covered periods, Remote Sens. Environ., 113, 2476–2498, 2009.
Salminen, M.: Remote sensing of snow: Factors influencing seasonal snow
mapping in boreal forest region. Doctoral dissertation, University of
Helsinki, Faculty of Science, Department of Geosciences and Geography &
Finnish Meteorological Institute Contributions No. 139, 126 pp., 2017.
Salminen, M., Pulliainen, J., Metsämäki, S., Kontu, A., and
Suokanerva, H.: The behaviour of snow and snow-free surface reflectance in
boreal forests: Implications to the performance of snow covered area
monitoring, Remote Sens. Environ., 113, 907–918, 2009.
Salminen, M., Pulliainen, J., Metsämäki, S., Ikonen, J., Heinilä, K.,
and Luojus, K.: Determination of uncertainty characteristics for the
satellite data-based estimation of fractional snow cover, Remote Sens.
Environ., 212, 103–113, 2018.
Sandmeier, St., Müller, Ch., Hosgood, B., and Andreoli, G.: Sensitivity
analysis and quality assessment of laboratory BRDF data, Remote Sens.
Environ., 64, 176–91, 1998.
Schaepman-Strub, G., Schaepman, M. E., Painter, T. H., Dangel, S., and
Martonchik, J. V.: Reflectance quantities in optical remote sensing –
definitions and case studies, Remote Sens. Environ., 103, 27–42, 2006.
Sihvola, A. and Tiuri, M.: Snow Fork for Field Determination of the Density
and Wetness Profiles of a Snow Pack, IEEE T. Geosci. Remote,
GE-24, 717–721, 1986.
Springsteen, A.: Standards for the measurement of diffuse reflectance – an
overview of available materials and measurement laboratories, Anal. Chim.
Acta, 380, 379–390, 1999.
Starks, P. J., Walter-Shea, E. A., Schiebe, F. R., and Markham, B. L.:
Temperature Sensitivity Characterization of a Silicon Diode Array
Spectrometer, Remote Sens. Environ., 51, 385–389, 1995.
Sukuvaara, T., Pulliainen, J., Kyrö, E., Suokanerva, H., Heikkinen, P.,
and Suomalainen, J.: Reflectance spectroradiometer measurement system in 30
meter mast for validating satellite images, Proceedings of the IEEE 2007
International Geoscience and Remote Sensing Symposium (IGARSS), Sensing And
Understanding Our Planet, Barcelona Spain,
1524–1528, 23–27 July 2007.
Tanikawa, T., Hori, M., Aoki, T., Hachikubo, A., Kuchiki, K., Niwano, M.,
Matoba, S., Yamaguchi, S., and Stamnes, K.: In-situ measurement of
polarization properties of snow surface under the Brewster geometry in
Hokkaido, Japan and northwest Greenland ice sheet, J. Geophys. Res.-Atmos.,
119, 13946–13964, 2014.
Virtanen, R., Oksanen, L., Oksanen, T., Cohen, J., Forbes, B. C., Johansen,
B., Käyhkö, J., Olofsson, J., Pulliainen, J., and Tømmervik, H.:
Where do the treeless tundra areas of northern highlands fit in the global
biome system: toward an ecologically natural subdivision of the tundra
biome, Ecol. Evol., 6, 143–158, 2016.
Wang, Z., Schaaf, C. B., Strahler, A. H., Chopping, M. J., Román, M. O.,
Shuai, Y., Woodcock, C. E., Hollinger, D. Y., and Fitzjarrald, D. R.:
Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and
forest surface types during dormant and snow-covered periods, Remote Sens.
Environ., 140, 60–77, 2014.
Wiscombe, W. J. and Warren, S. G.: A Model for the Spectral Albedo of Snow.
I: Pure Snow, J. Atmos. Sci., 37, 2712–2733, 1980.
Short summary
We publish and describe a surface spectral reflectance data record of seasonal snow (dry, wet, shadowed), forest ground (lichen, moss) and forest canopy (spruce and pine, branches) constituting the main elements of the boreal landscape and collected at four scales. The data record describes the characteristics and variability of the satellite scene reflectance contributors in boreal landscape, thus enabling the development of improved optical satellite snow mapping methods for forested areas.
We publish and describe a surface spectral reflectance data record of seasonal snow (dry, wet,...
Altmetrics
Final-revised paper
Preprint